Detection and enumeration of categories and types of blood cells comprising whole blood has provided extensive challenge to designers and manufacturers of automated hematology instruments. An approach to automated hematology which is increasingly finding acceptance as the preferred approach is one often designated as optical flow cytometry. Such systems employ a hydrodynamically focused stream in which blood cells are passed extremely rapidly, one at a time. The fluid stream is illuminated by precisely focused light, for example coherent radiation from a laser and the changes effected to such focused light by the passage of the cells is detected. Much can be determined by analysis of light scattered by the cells, and if the blood sample has been treated with specific staining agents, still more can be determined by a suitable analysis of fluorescent light stimulated from a stained cell or other fluorescent material passing through the focusing zone. Often the populations of cells passing through the focusing zone may be discriminated based on their relative ability to fluorescently stain or the inherent presence of fluorescing material within the cell thereby exhibiting distinct levels of fluorescence. Each such population by itself can be expected to exhibit a characteristic histogram relating fluorescent levels with the number of cells present. As is sometimes the case, however, mixtures of such populations will exhibit a histogram composed of overlapping subpopulation histograms. In these instances, it may be very difficult to separate the individual populations by means of histogram analysis.
One approach to this problem, taught in U.S. Pat. No. 4,325,706 to R. J. Gershman, et al entitled "Automated Detection of Platelets and Reticulocytes in Whole Blood, (commonly assigned)" involves making assumptions as to the distribution of the cells versus associated fluorescent light stimulations and thereaftrer fitting a Gaussian curve to the curve of the distribution at a point centering with its peak and then detecting deviations from said Gaussian curve by predetermined statistical functions.
It is an object of the present invention to provide yet another automated, effective and highly repeatable approach to the discrimination between subpopulations of cells whose individual histograms have been combined to form a common histogram.